Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms

Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks. For audio signals, the approach takes raw waveforms as input using an 1-D convolution layer. In this paper, we improve the 1-D CNN architecture for music auto-tagging by adopting building blocks from state-of-the-art image classification models, ResNets and SENets, and adding multi-level feature aggregation to it. We compare different combinations of the modules in building CNN architectures. The results show that they achieve significant improvements over previous state-of-the-art models on the MagnaTagATune dataset and comparable results on Million Song Dataset. Furthermore, we analyze and visualize our model to show how the 1-D CNN operates.Comment: Accepted for publication at ICASSP 201

Temporal Feedback Convolutional Recurrent Neural Networks for Keyword Spotting

While end-to-end learning has become a trend in deep learning, the model architecture is often designed to incorporate domain knowledge. We propose a novel convolutional recurrent neural network (CRNN) architecture with temporal feedback connections, inspired by the feedback pathways from the brain to ears in the human auditory system. The proposed architecture uses a hidden state of the RNN module at the previous time to control the sensitivity of channel-wise feature activations in the CNN blocks at the current time, which is analogous to the mechanism of the outer hair-cell. We apply the proposed model to keyword spotting where the speech commands have sequential nature. We show the proposed model consistently outperforms the compared model without temporal feedback for different input/output settings in the CRNN framework. We also investigate the details of the performance improvement by conducting a failure analysis of the keyword spotting task and a visualization of the channel-wise feature scaling in each CNN block.Comment: This paper is submitted to ICASSP 202

Multiphoton tissue imaging by using moxifloxacin

Multiphoton microscopy has been widely used for in-vivo tissue imaging of various biological studies. However, its application to clinical studies has been limited due to either lack of clinically compatible exogenous contrast agents or weak autofluorescence of tissues. We investigated moxifloxacin as a contrast agent of cells for multiphoton tissue imaging. Moxifloxacin is an FDA approved antibiotic with relatively good pharmacokinetic properties for tissue penetration and intrinsic fluorescence. Two-photon microscopy (TPM) of moxifloxacin treated mouse corneas showed good tissue penetration and high concentration inside the corneal cells [1]. Cell labeling of moxifloxacin was tested in both cultured cells and isolated immune cells. Moxifloxacin tissue applications were tested in various mouse organs such as the skin, small intestine, and brain. Most of tissues were labeled well via topical administration, and only the skin required additional gentle removal of the outermost stratum corneum by tape stripping. TPM of these tissues showed non-specific cell labeling of moxifloxacin and fluorescence enhancement [2]. Although most of experimental results were from mouse tissues, its clinical application would be possible. Clinical application is promising since imaging based on moxifloxacin labeling could be 10 times faster than imaging based on endogenous fluorescence. Moxifloxacin labeling of cultured cells was demonstrated by comparing TPM images with and without moxifloxacin treatment. Bright fluorescence inside cells were observed only with moxifloxacin at the same imaging condition. TPM of the skin dermis visualized many dermal cells with increased fluorescence, and TPM of the villus in the small intestine showed the covering epithelial cells and cells inside the villus clearly. Please click Additional Files below to see the full abstract

An improved Bayesian inference model for auto healing of concrete specimen due to a cyclic freeze-thaw experiment

This paper presents an innovative solution for the auto healing porous structures damaged by cyclic freeze-thaw, followed by predicting the results of recovered damage due to freezing based on Bayesian inference. The additional hydration of high strength material, cured in high temperature, is applied as auto curing for the damaged micro-pore structures. Modeling of micro pore structure is prior to damage analysis. The amount of ice volume with temperature dependent surface tensions, freezing pressure and resulting deformations, and cycle and temperature dependent pore volume has been predicted and compared with available test results. By heating the selected area of specimen in frozen chamber, approximately 100 % of strength recovery has been observed after 10 days of freeze-thaw tests in the proposed nonlinear stochastic prediction models and the experimental results

Reliability assessment based on an adaptive response surface method considering correlation among random variables

Although the Monte-Carlo Simulation (MCS) technique can evaluate a reliability of most structural systems, its processing time equals, approximately, the reciprocal of the probability of failure. While the Stochastic Finite Element (SFE) method could help to solve such a drawback, it is limited to specific computer programs, in which the mean and the coefficient of random variables are estimated by a perturbation, or by a weighted integral method. Therefore, SFE may not be easily applicable when using commercial software or systems that are not prepared with the prerequisite programming. To overcome these limitations, the RSM can be applied, because its accuracy depends on both the distance of axial points, and the linearity of the Limit State Functions (LSFs). The correlation among random variables and the response of a system is evaluated by composing a Bayesian belief nets (BBN). Consequently, the proposed Linear Adaptive Weighted Response Surface Method (LAW-RSM) with BBN modeling produces improved converged reliability indices than conventional RSMs and detail observation for the uncertainties in structural components

X-SNS: Cross-Lingual Transfer Prediction through Sub-Network Similarity

Cross-lingual transfer (XLT) is an emergent ability of multilingual language models that preserves their performance on a task to a significant extent when evaluated in languages that were not included in the fine-tuning process. While English, due to its widespread usage, is typically regarded as the primary language for model adaption in various tasks, recent studies have revealed that the efficacy of XLT can be amplified by selecting the most appropriate source languages based on specific conditions. In this work, we propose the utilization of sub-network similarity between two languages as a proxy for predicting the compatibility of the languages in the context of XLT. Our approach is model-oriented, better reflecting the inner workings of foundation models. In addition, it requires only a moderate amount of raw text from candidate languages, distinguishing it from the majority of previous methods that rely on external resources. In experiments, we demonstrate that our method is more effective than baselines across diverse tasks. Specifically, it shows proficiency in ranking candidates for zero-shot XLT, achieving an improvement of 4.6% on average in terms of NDCG@3. We also provide extensive analyses that confirm the utility of sub-networks for XLT prediction.Comment: Accepted to EMNLP 2023 (Findings

Improvement of P300-Based Brain-Computer Interfaces for Home Appliances Control by Data Balancing Techniques

The oddball paradigm used in P300-based brain-computer interfaces (BCIs) intrinsically poses the issue of data imbalance between target stimuli and nontarget stimuli. Data imbalance can cause overfitting problems and, consequently, poor classification performance. The purpose of this study is to improve BCI performance by solving this data imbalance problem with sampling techniques. The sampling techniques were applied to BCI data in 15 subjects controlling a door lock, 15 subjects an electric light, and 14 subjects a Bluetooth speaker. We explored two categories of sampling techniques: oversampling and undersampling. Oversampling techniques, including random oversampling, synthetic minority oversampling technique (SMOTE), borderline-SMOTE, support vector machine (SVM) SMOTE, and adaptive synthetic sampling, were used to increase the number of samples for the class of target stimuli. Undersampling techniques, including random undersampling, neighborhood cleaning rule, Tomek's links, and weighted undersampling bagging, were used to reduce the class size of nontarget stimuli. The over- or undersampled data were classified by an SVM classifier. Overall, some oversampling techniques improved BCI performance while undersampling techniques often degraded performance. Particularly, using borderline-SMOTE yielded the highest accuracy (87.27%) and information transfer rate (8.82 bpm) across all three appliances. Moreover, borderline-SMOTE led to performance improvement, especially for poor performers. A further analysis showed that borderline-SMOTE improved SVM by generating more support vectors within the target class and enlarging margins. However, there was no difference in the accuracy between borderline-SMOTE and the method of applying the weighted regularization parameter of the SVM. Our results suggest that although oversampling improves performance of P300-based BCIs, it is not just the effect of the oversampling techniques, but rather the effect of solving the data imbalance problem